Emotion recognition in conversation is important for an empathetic dialogue system to understand the user’s emotion and then generate appropriate emotional responses. However, most previous researches focus on modeling conversational contexts primarily based on the textual modality or simply utilizing multimodal information through feature concatenation. In order to exploit multimodal information and contextual information more effectively, we propose a multimodal directed acyclic graph (MMDAG) network by injecting information flows inside modality and across modalities into the DAG architecture. Experiments on IEMOCAP and MELD show that our model outperforms other state-of-the-art models. Comparative studies validate the effectiveness of the proposed modality fusion method.

命名实体识别是文学作品智能分析的基础性工作,当前文学领域命名实体识别的研究还较薄弱,一个主要的原因是缺乏标注语料。本文从金庸小说入手,对两部小说180余万字进行了命名实体的标注,共标注4类实体5万多个。针对小说文本的特点,本文提出融入篇章信息的命名实体识别模型,引入篇章字典保存汉字的历史状态,利用可信度计算融合BiGRU-CRF与Transformer模型。实验结果表明,利用篇章信息有效地提升了命名实体识别的效果。最后,我们还探讨了命名实体识别在小说社会网络构建中的应用。